Tool call

In an agentic system the tool call is the boundary between reasoning and action. Everything before it is a model's proposal; the call is what actually reads a database, spends money or writes a record. Treating it as the unit of accounting rather than treating the whole conversation as one is what makes an agent's behaviour reviewable afterwards.

Three properties are worth designing for. A call should be idempotent or carry an idempotency key, because agents retry. Its result should be structured rather than prose, so the next step branches on a field instead of on a parsed sentence. And its cost should be predictable, so an agent looping over a file cannot produce a surprising bill.

The most important design question for a resolution tool is what it returns when it does not know. A tool that always returns a best guess with a score forces the agent to invent a threshold, which it will do badly and inconsistently. A tool that returns a distinct unresolved outcome lets the agent branch on the decision itself: act on a match, escalate or enrich an abstention, stop on a non-match.

Entity Bind's tools return the decision rather than a similarity score, and the credit rule follows the work done: one credit for a decision reached deterministically or probabilistically, five where LLM adjudication or agentic research was needed, and zero for an abstention, a byte-identical replay, or an input that normalised to no identifying signal. Branch on the decision first and evaluate any stricter confidence policy against labelled records. Confidence bounds depend on the recorded evidence population; there is no universal score for every match. Read match_confidence_basis and the evidence. A 0.99 filter can return some v8 adjudicated matches while excluding valid identifier matches, so validate its coverage cost on your own inputs.